# Efficient Least Squares for Estimating Total Effects under Linearity and Causal Sufficiency.

@article{Guo2020EfficientLS, title={Efficient Least Squares for Estimating Total Effects under Linearity and Causal Sufficiency.}, author={F. Richard Guo and Emilija Perkovi'c}, journal={arXiv: Statistics Theory}, year={2020} }

Recursive linear structural equation models are widely used to postulate causal mechanisms underlying observational data. In these models, each variable equals a linear combination of a subset of the remaining variables plus an error term. When there is no unobserved confounding or selection bias, the error terms are assumed to be independent. We consider estimating a total causal effect in this setting. The causal structure is assumed to be known only up to a maximally oriented partially…

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